Huck Institutes Life Sciences NSF NCEMS Postdoctoral Scholar-Computational Proteomics​/Mass Spectrometry

Position: Huck Institutes of the Life Sciences NSF NCEMS Postdoctoral Scholar-Computational Proteomics/Mass Spectrometry
Location: University Park

APPLICATION INSTRUCTIONS:

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* CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.

* If you are NOT a current employee or student, please click "Apply" and complete the application process for external applicants.

Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.

This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.

POSITION SPECIFICS

Postdoctoral Scholar (Computational Proteomics/Mass Spectrometry)

The National Synthesis Center for Emergence in the Molecular and Cellular Sciences (NCEMS) at The Pennsylvania State University (ncems.psu.edu) seeks an outstanding scientist to fill a Postdoctoral Scholar position in Computational Proteomics and Mass Spectrometry housed within the Huck Institutes of Life Sciences at The Pennsylvania State University.

NCEMS is an NSF-funded interdisciplinary research center positioned at the interface of data science, molecular and cellular biology, and quantitative physical sciences. The Center provides leadership in the integration of diverse, publicly available datasets to gain insights into emergent phenomena in molecular and cellular biology.

About the Position

The successful candidate, in collaboration with faculty and research staff at NCEMS, will conduct original research involving proteomics, mass spectrometry data analysis, protein interactions, protein complexes, post-translational modifications, structural proteomics, or systems-level analysis of molecular and cellular biology. Research may involve large-scale public proteomics datasets, data harmonization, cross-study integration, quantitative proteomics, crosslinking mass spectrometry, interactomics, protein structure and function, molecular assemblies, or biological networks.

The scholar's independent work may use, extend, or contribute to NCEMS Working Group data products, analytical tools, computational workflows, and methodological innovations. The scholar will also collaborate with NCEMS Working Groups where their expertise can help advance shared research goals, strengthen proteomics-based discovery, and produce high-impact scientific outputs.

This position is full time, on-site at the Penn State University Park campus. This position does not permit remote work. This is a term appointment funded for one year from the date of hire with an excellent chance for multiple years of renewal based upon mutual agreement.

Requirements

A PhD in a proteomics' relevant field is required. The successful candidate will have a strong foundation in proteomics, mass spectrometry, protein biochemistry, quantitative biology, bioinformatics, or related analytical and computational approaches. An ability to conduct research and produce scholarly outputs as demonstrated through publications in peer-reviewed journals. Candidates should have experience analyzing, interpreting, or integrating mass spectrometry-based proteomics data.

Experience with mass spectrometry-based workflows is expected, such as bottom-up proteomics, quantitative proteomics, targeted or untargeted proteomics, post-translational modification analysis, crosslinking mass spectrometry, structural proteomics, protein-protein interaction analysis, or related approaches. Expertise in computational analysis of proteomics data is highly desirable.

Experience with Python, R, or related scientific computing tools; proteomics software platforms; biological databases; data repositories; reproducible workflows; or large-scale public proteomics datasets is desirable. Familiarity with public proteomics…